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» Evaluating learning algorithms and classifiers
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118
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GECCO
2003
Springer
103views Optimization» more  GECCO 2003»
15 years 9 months ago
MILA - Multilevel Immune Learning Algorithm
The biological immune system is an intricate network of specialized tissues, organs, cells, and chemical molecules. T-cell-dependent humoral immune response is one of the complex i...
Dipankar Dasgupta, Senhua Yu, Nivedita Sumi Majumd...
146
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JMLR
2012
13 years 7 months ago
Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets
We propose a new method for comparing learning algorithms on multiple tasks which is based on a novel non-parametric test that we call the Poisson binomial test. The key aspect of...
Alexandre Lacoste, François Laviolette, Mar...
MLMI
2004
Springer
15 years 10 months ago
Shallow Dialogue Processing Using Machine Learning Algorithms (or Not)
This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and ...
Andrei Popescu-Belis, Alexander Clark, Maria Georg...
SGAI
2004
Springer
15 years 10 months ago
Interactive Selection of Visual Features through Reinforcement Learning
We introduce a new class of Reinforcement Learning algorithms designed to operate in perceptual spaces containing images. They work by classifying the percepts using a computer vi...
Sébastien Jodogne, Justus H. Piater
IJCAI
2003
15 years 6 months ago
A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data
This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.11b network. Our approach uses data labeled by ground trut...
Sebastian Thrun, Geoffrey J. Gordon, Frank Pfennin...